Repository navigation
Create the eigensolver start vector on the device of the matrix - #722
Merged
Merged
Conversation
scaled_laplacian estimates the largest eigenvalue with KrylovKit, starting from a random vector that was always created on the CPU. For a GPU graph, Symmetric(A) * x0 then went to CPU BLAS with a GPU matrix. CUDA avoided this with its own method in the extension, but on AMDGPU OpenBLAS read GPU memory and the process crashed with a segmentation fault, which is why the GNNGraphs AMDGPU job has failed in every build since at least August. The start vector is now allocated with similar, so it lives where the matrix does on any backend. Sparse matrices keep a dense CPU vector, since similar would give a sparse one.
Member
|
Nice! |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This solves a bug which has been failing AMDGPU CI for a few months.